| | --- |
| | license: apache-2.0 |
| | --- |
| | # Thyroid Ultrasonograph Image Classifier |
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| | **Author:** Afif Ali Saadman |
| | **Type:** Deep Learning (Modified AlexNet variant) |
| | **Framework:** PyTorch |
| | **Date:** October 2025 |
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|
| | ## Overview |
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| | **This model was developed as part of an **independent research project** focused on classifying thyroid ultrasound images into multiple diagnostic categories using deep learning.** |
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| | The model can identify: |
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| | - **FTC** – Follicular Thyroid Carcinoma |
| | - **PTC** – Papillary Thyroid Carcinoma |
| | - **MTC** – Medullary Thyroid Carcinoma |
| | - **Benign** – Non-cancerous thyroid tissue |
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| | ## Architecture |
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| | NOTE: This model was trained on a T4 GPU in google colaboratory. |
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| | * Model : This model was trained on a AlexNet like architecture with gradient checkpointing. |
| | * Loss Function: Cross Entropy Loss |
| | * Learning Rate: 0.0001 |
| | * Iters(Epochs): 20 |
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| | ## Dataset |
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| | This dataset was extracted from `FangDai/Thyroid_Ultrasound_Images `and `agent593/Thyroid-Ultrasound-Image-Classification-ViTModel/tree/main/dataset%20thyroid/` which were cleaned manually. |
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| | 1. **FTC (Follicular Thyroid Carcinoma) – 100 images** |
| | 2. **PTC (Papillary Thyroid Carcinoma) – 99 images** |
| | 3. **MTC (Medullary Thyroid Carcinoma) – 99 images** |
| | 4. **Benign (Normal Thyroid) - 90 images** |
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| | ## Confusion Matrix |
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| | ## Classification Report |
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| | | Class | Precision | Recall | F1-Score | Support | |
| | | ---------------- | --------- | ------ | -------- | ------- | |
| | | FTC | 0.93 | 0.93 | 0.93 | 15 | |
| | | PTC | 0.88 | 0.70 | 0.78 | 10 | |
| | | MTC | 0.80 | 0.80 | 0.80 | 10 | |
| | | Benign | 0.88 | 1.00 | 0.94 | 15 | |
| | | **Accuracy** | - | - | 0.88 | 50 | |
| | | **Macro Avg** | 0.87 | 0.86 | 0.86 | 50 | |
| | | **Weighted Avg** | 0.88 | 0.88 | 0.88 | 50 | |
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| | ## Final Report |
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| | Benign: perfect classification (15/15) |
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| | FTC: only one misclassified |
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| | PTC: 2 misclassified (one as FTC, one as Benign) |
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| | MTC: also strong, only a few mislabels |
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| | ## More information |
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| | For more information, kindly see this notebook:[USGResearch.ipynb · Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model at main](https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model/blob/main/USGResearch.ipynb) |
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| | ## Where you can find this model? |
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| | **HuggingFace**:[Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model · Hugging Face](https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model) |
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| | Kaggle: [Afif Ali Saadman | Thyroid\_Canciroma\_Classifier | Kaggle](https://www.kaggle.com/models/afifalisaadman/thyroid-canciroma-classifier/) |
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| | ## Citation |
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|
| | ``` |
| | @misc{saadman2025thyroid, |
| | author = {Afif Ali Saadman}, |
| | title = {Thyroid Ultrasonograph Image Classifier}, |
| | year = {2025}, |
| | month = {October}, |
| | note = {Deep Learning (Modified AlexNet variant), PyTorch. Available at \url{https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model} and \url{https://www.kaggle.com/models/afifalisaadman/thyroid-canciroma-classifier/}} |
| | } |
| | |
| | ``` |
